Computer vision-based algorithm to sUppoRt coRrect electrode placemeNT (CURRENT) for home-based electric non-invasive brain stimulation

Fabienne Windel, Rémy Marc M. Gardier, Gaspard Fourchard, Roser Viñals, Daphne Bavelier, Frank Johannes Padberg, Elmars Rancans, Omer Bonne, Mor Nahum, Jean Philippe Thiran, Takuya Morishita, Friedhelm Christoph Hummel*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Objective: Home-based non-invasive brain stimulation (NIBS) has been suggested as an adjunct treatment strategy for neuro-psychiatric disorders. There are currently no available solutions to direct and monitor correct placement of the stimulation electrodes. To address this issue, we propose an easy-to-use digital tool to support patients for self-application. Methods: We recruited 36 healthy participants and compared their cap placement performance with the one of a NIBS-expert investigator. We tested participants’ placement accuracy with instructions before (Pre) and after the investigator's placement (Post), as well as participants using the support tool (CURRENT). User experience (UX) and confidence were further evaluated. Results: Permutation tests demonstrated a smaller deviation within the CURRENT compared with Pre cap placement (p = 0.02). Subjective evaluation of ease of use and usefulness of the tool were vastly positive (8.04 out of 10). CURRENT decreased the variability of performance, ensured placement within the suggested maximum of deviation (10 mm) and supported confidence of correct placement. Conclusions: This study supports the usability of this novel technology for correct electrode placement during self-application in home-based settings. Significance: CURRENT provides an exciting opportunity to promote home-based, self-applied NIBS as a safe, high-frequency treatment strategy that can be well integrated in patients’ daily lives.

Original languageAmerican English
Pages (from-to)57-67
Number of pages11
JournalClinical Neurophysiology
StatePublished - Sep 2023

Bibliographical note

Publisher Copyright:
© 2023


  • Computer vision
  • Electrode localization algorithm
  • Home-based non-invasive brain stimulation
  • Monitoring
  • Real-time feedback
  • tES


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